3D preoperative imaging (e.g., CT, MRI, PET) provides the basis for many forms of surgical planning and intraoperative guidance. During the surgical planning stage, clinicians may define geometric annotations (points, contours, shapes, etc.) in the preoperative 3D images, such as defining a point on an anatomical structure of interest, outlining relevant anatomical structures, and identifying the desired placement/trajectory of surgical hardware. These annotations can be incorporated into the surgical process using, for example, via a mechanism using various algorithms for rigid/deformable registration (aligning physical coordinate systems to create a “mapping” from one coordinate system to another). In the context of surgical guidance, it is often useful to create such a mapping between the 3D preoperative images and the 2D intraoperative images (e.g., x-ray radiographic/fluoroscopy systems). This is referred to as 3D-2D image registration. This mapping then enables the annotations defined in the 3D image to be overlaid onto the 2D image, providing decision support for the clinician as well as a means for verification of the surgical product.
There are previously established methods to achieve 3D-2D image registration that map preoperative 3D images to the space of the 2D image—for example, mapping a preoperative CT image onto the corresponding coordinates in a 2D projection intraoperative x-ray radiograph. In principle, such registration methods can be categorized as: 1) intensity-based and/or 2) feature-based. In intensity-based registration, the voxel values (i.e., image “intensity” value in the 3D image) and the pixel values (i.e., the image “intensity” values in the 2D image) are used directly in comparison of similarity and alignment; on the other hand, in feature-based registration, the registration is performed using a set of features (usually point sets, contours, and/or surfaces) extracted from one or both of the images.
3D-2D registration is potentially valuable in medical interventions, such as surgery and radiation therapy. For example, in spine surgery, the “LevelCheck” method uses 3D-2D registration to map vertebral level locations that have been defined in the preoperative CT onto 2D radiographic images. This registration assists the surgeon during target localization by identifying specific vertebral levels, and provides advantages in terms of time, dose, and accuracy compared to manual level counting.
In applications such as accurate vertebral level localization, in addition to the preoperative and intraoperative images, there are annotations defined within the 3D preoperative image (e.g., a label defined on each vertebra in the 3D image), and the goal of 3D-2D registration is to map the location of such annotations to the 2D image. Thus, in these applications, registration is intended to map the annotation locations rather than physically aligning the entire content captured in the images. Accordingly, the performance of the registration process can be quantified in terms of the accuracy of mapping each annotation.
Existing methods for image-intensity-based 3D-2D annotation mapping have used a rigid transformation and can be limited by deformation in anatomy. These deformations occur commonly due to differences in patient positioning between the preoperative acquisition and intraoperative acquisition during surgery. Other sources of deformation include patient motion, breathing, or the procedure itself—e.g., correction of spinal curvature. For example, 3D images are often acquired when the patient is lying in a supine position (on the CT scanner table), whereas surgery often requires the patient to lie in a prone position (on the OR table). This results in a deformation of anatomy, including the structures of interest in surgery (e.g., the spinal vertebrae). Because the patient anatomy presented in the 2D image is deformed with respect to the 3D image, a single rigid transformation between the 3D and 2D space does not describe the complexity of motion, and the accuracy of annotation mapping can be degraded. Deformable image registration could improve accuracy in these scenarios, but such methods are susceptible to local optima and often fail due to a large number of parameters being optimized and an inherent degeneracy between magnification and object enlargement. Piece-wise rigid registration methods exist in feature-based registration to account for these deformations; however, segmentations or shape models need to be extracted from the 3D image to perform the registration, creating additional work in planning, introducing sources of error in segmentation, and often disregarding potentially relevant image intensity information.
It would therefore be advantageous to provide a solution that accomplishes a globally deformable registration of annotated structures from the 3D image to the 2D space. This invention describes a method for accomplishing such globally deformable registration of annotations by way of multiple locally rigid registrations.